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<table width="100%" summary="page for vaso"><tr><td>vaso</td><td align="right">R Documentation</td></tr></table>

<h2>Vaso Constriction Data Set</h2>

<h3>Description</h3>


<p>Finney's data on vaso constriction in the skin of the digits.
</p>


<h3>Usage</h3>

<pre>data(vaso)</pre>


<h3>Format</h3>


<p>A data frame with 39 observations on the following 3 variables.
</p>

<dl>
<dt><code>Volume</code></dt><dd><p>Inhaled volume of air</p>
</dd>
<dt><code>Rate</code></dt><dd><p>Rate of inhalation</p>
</dd>
<dt><code>Y</code></dt><dd><p>vector of 0 or 1 values.</p>
</dd>
</dl>



<h3>Details</h3>

<p>The data taken from Finney (1947) were obtained in a carefully
controlled study in human physiology where a reflex
&ldquo;vaso constriction&rdquo; may occur in the skin of the digits after taking a
single deep breath.  The response y is the occurence (y = 1) or
non-occurence (y = 0) of vaso constriction in the skin of the digits
of a subject after he or she inhaled a certain volume of air at a certain
rate.  The responses of three subjects are available.  The first
contributed 9 responses, the second contributed 8 responses, and the
third contributed 22 responses.
</p>
<p>Although the data represent repeated measurements, an analysis that
assumes independent observations may be applied, as claimed by Pregibon
(1981).
</p>


<h3>Source</h3>


<p>Finney, D.J. (1947)
The estimation from individual records of the relationship between
dose and quantal response.
<EM>Biometrika</EM> <B>34</B>, 320&ndash;334
</p>


<h3>References</h3>


<p>Atkinson, A.C. and Riani, M. (2000)
<EM>Robust Diagnostic Regression Analysis</EM>,
First Edition. New York: Springer, Table A.23.
</p>
<p>Fahrmeir, L. and Tutz, G. (2001)
<EM>Multivariate Statistical Modelling Based on Generalized Linear Models</EM>,
Springer, Table 4.2.
</p>
<p>Kuensch, H.R., Stefanski, A. and Carrol, R.J. (1989)
Conditionally unbiased bounded influence estimation in general
regression models, with applications to generalized linear models,
<EM>JASA</EM> <B>84</B>, 460&ndash;466.
</p>
<p>Pregibon, D. (1981)
Logistic regression diagnostics,
<EM>Annals of Statistics</EM> <B>9</B>, 705&ndash;724.
</p>


<h3>Examples</h3>

<pre>
data(vaso)
str(vaso)
pairs(vaso)

glmV &lt;- glm(Y ~ log(Volume) + log(Rate), family=binomial, data=vaso)
summary(glmV)
## --&gt;  example(glmrob)  showing classical &amp; robust GLM
</pre>


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